def batch_predict_loop(model, inputs, batch_size, verbose=0):
"""Predict function for eager execution when input is arrays or tensors.
Arguments:
model: Instance of `Model`.
inputs: List of input arrays.
batch_size: Integer batch size.
verbose: Verbosity mode.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions (if the model has multiple outputs).
"""
outs = []
num_samples = training_utils.check_num_samples(inputs, batch_size)
if verbose == 1:
progbar = generic_utils.Progbar(target=num_samples)
batches = generic_utils.make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
inputs_batch = slice_arrays(inputs, batch_ids)
inputs_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in inputs_batch
]
if len(inputs_batch) == 1:
if model._expects_training_arg:
batch_outs = model.call(inputs_batch[0], training=False)
else:
batch_outs = model.call(inputs_batch[0])
else:
if model._expects_training_arg:
batch_outs = model.call(inputs_batch, training=False)
else:
batch_outs = model.call(inputs_batch)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if batch_index == 0:
# Pre-allocate the results arrays.
for batch_out in batch_outs:
dims = batch_out.shape[1:].dims
dims_list = [d.value for d in dims]
shape = (num_samples, ) + tuple(dims_list)
outs.append(
np.zeros(shape, dtype=batch_out.dtype.as_numpy_dtype))
for i, batch_out in enumerate(batch_outs):
outs[i][batch_start:batch_end] = batch_out
if verbose == 1:
progbar.update(batch_end)
if len(outs) == 1:
return outs[0]
return outs
def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model:
inputs: List of input arrays.
batch_size: integer batch size.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
with backend.learning_phase_scope(0):
num_samples = training_utils.check_num_samples(inputs, batch_size,
steps, 'steps')
if verbose == 1:
if steps is not None:
progbar = generic_utils.Progbar(target=steps)
else:
progbar = generic_utils.Progbar(target=num_samples)
outs = []
batches = generic_utils.make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
inputs_batch = slice_arrays(inputs, batch_ids)
inputs_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in inputs_batch
]
if len(inputs_batch) == 1:
if model._expects_training_arg:
batch_outs = model.call(inputs_batch[0], training=False)
else:
batch_outs = model.call(inputs_batch[0])
else:
if model._expects_training_arg:
batch_outs = model.call(inputs_batch, training=False)
else:
batch_outs = model.call(inputs_batch)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if batch_index == 0:
# Pre-allocate the results arrays.
for batch_out in batch_outs:
dims = batch_out.shape[1:].dims
dims_list = [d.value for d in dims]
shape = (num_samples, ) + tuple(dims_list)
outs.append(
np.zeros(shape, dtype=batch_out.dtype.as_numpy_dtype))
for i, batch_out in enumerate(batch_outs):
outs[i][batch_start:batch_end] = batch_out
if verbose == 1:
progbar.update(batch_end)
if len(outs) == 1:
return outs[0]
return outs
def test_loop(model,
inputs,
targets,
sample_weights=None,
batch_size=None,
verbose=0,
steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Model instance that is being evaluated in Eager mode.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
batch_size: integer batch size or `None`.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
"""
with backend.learning_phase_scope(0):
feed_data = inputs + targets
if sample_weights:
feed_data += sample_weights
num_samples = training_utils.check_num_samples(feed_data,
batch_size=batch_size,
steps=steps,
steps_name='steps')
outs = []
if verbose == 1:
progbar = generic_utils.Progbar(target=num_samples)
batches = generic_utils.make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
inputs_batch = slice_arrays(inputs, batch_ids)
targets_batch = slice_arrays(targets, batch_ids)
if sample_weights:
sample_weights_batch = slice_arrays(sample_weights, batch_ids)
else:
sample_weights_batch = None
inputs_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in inputs_batch
]
targets_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in targets_batch
]
if sample_weights:
sample_weights_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
if val is not None else None
for val in sample_weights_batch
]
loss_outs, loss, loss_metrics = _model_loss(
model,
inputs_batch,
targets_batch,
sample_weights=sample_weights_batch,
training=False)
metrics_results = _eager_metrics_fn(model, loss_outs,
targets_batch)
batch_outs = []
for _, v in zip(model.metrics_names, [backend.mean(loss)] +
loss_metrics + metrics_results):
batch_outs.append(tensor_util.constant_value(v))
if isinstance(batch_outs, list):
if batch_index == 0:
for batch_out in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
outs[i] += batch_out * len(batch_ids)
else:
if batch_index == 0:
outs.append(0.)
outs[0] += batch_outs * len(batch_ids)
if verbose == 1:
progbar.update(batch_end)
for i in range(len(outs)):
outs[i] /= num_samples
if len(outs) == 1:
return outs[0]
return outs
def iterator_predict_loop(model, inputs, steps, verbose=0):
"""Predict function for eager execution when input is dataset iterator.
Arguments:
model: Instance of `Model`.
inputs: Input dataset iterator.
steps: Total number of steps (batches of samples) before declaring
`_predict_loop` finished.
verbose: Verbosity mode.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions (if the model has multiple outputs).
Raises:
ValueError: In case of mismatch between given number of inputs and
expectations of the model.
"""
assert isinstance(inputs, iterator_ops.EagerIterator)
outs = []
if verbose == 1:
progbar = generic_utils.Progbar(target=steps)
for step_index in range(steps):
# Get data from the iterator.
try:
next_element = inputs.get_next()
except errors.OutOfRangeError:
logging.warning(
'Your dataset iterator ran out of data; '
'interrupting prediction. Make sure that your '
'dataset can generate at least `steps` '
'batches (in this case, %d batches).', steps)
break
if not isinstance(next_element,
(list, tuple)) or len(next_element) != 2:
raise ValueError(
'Please provide data as a list or tuple of 2 elements '
' - input and target pair. Received %s. We do not use the '
'`target` value here.' % next_element)
x, _ = next_element
# Validate and standardize data.
x, _, _ = model._standardize_user_data(x)
if model._expects_training_arg:
batch_outs = model.call(x[0] if len(x) == 1 else x, training=False)
else:
batch_outs = model.call(x[0] if len(x) == 1 else x)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
# We collect the results from every step and then concatenate them once
# in the end. This is an expensive process. We are doing this because we
# do not know the number of samples beforehand.
if step_index == 0:
for _ in batch_outs:
outs.append([])
for i, batch_out in enumerate(batch_outs):
outs[i].append(backend.get_value(batch_out))
if verbose == 1:
progbar.update(step_index + 1)
for i, out in enumerate(outs):
outs[i] = np.concatenate(tuple(out), axis=0)
if len(outs) == 1:
return outs[0]
return outs
def batch_test_loop(model,
inputs,
targets,
batch_size,
sample_weights=None,
verbose=0):
"""Test function for eager execution when input is given as arrays or tensors.
Arguments:
model: Model instance that is being evaluated in Eager mode.
inputs: List of input arrays.
targets: List of target arrays.
batch_size: Integer batch size.
sample_weights: Optional list of sample weight arrays.
verbose: Verbosity mode.
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
"""
outs = []
feed_data = inputs + targets
if sample_weights:
feed_data += sample_weights
num_samples = training_utils.check_num_samples(feed_data,
batch_size=batch_size)
if verbose == 1:
progbar = generic_utils.Progbar(target=num_samples)
batches = generic_utils.make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
inputs_batch = slice_arrays(inputs, batch_ids)
targets_batch = slice_arrays(targets, batch_ids)
if sample_weights:
sample_weights_batch = slice_arrays(sample_weights, batch_ids)
else:
sample_weights_batch = None
inputs_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in inputs_batch
]
targets_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in targets_batch
]
if sample_weights:
sample_weights_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
if val is not None else None for val in sample_weights_batch
]
loss_outs, loss, loss_metrics = _model_loss(
model,
inputs_batch,
targets_batch,
sample_weights=sample_weights_batch,
training=False)
metrics_results = _eager_metrics_fn(model, loss_outs, targets_batch)
batch_outs = []
for _, v in zip(model.metrics_names,
[backend.mean(loss)] + loss_metrics + metrics_results):
batch_outs.append(tensor_util.constant_value(v))
if isinstance(batch_outs, list):
if batch_index == 0:
for _ in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
outs[i] += batch_out * len(batch_ids)
else:
if batch_index == 0:
outs.append(0.)
outs[0] += batch_outs * len(batch_ids)
if verbose == 1:
progbar.update(batch_end)
for i in range(len(outs)):
outs[i] /= num_samples
if len(outs) == 1:
return outs[0]
return outs
def iterator_test_loop(model, inputs, steps, verbose=0):
"""Test function for eager execution when input is given as dataset iterator.
Arguments:
model: Model instance that is being evaluated in Eager mode.
inputs: Input dataset iterator.
steps: Total number of steps (batches of samples) before declaring
predictions finished.
verbose: Verbosity mode.
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
Raises:
ValueError: In case of mismatch between given number of inputs and
expectations of the model.
"""
assert isinstance(inputs, iterator_ops.EagerIterator)
outs = []
num_samples = 0
if verbose == 1:
progbar = generic_utils.Progbar(target=steps)
for step_index in range(steps):
# Get data from the iterator.
try:
next_element = inputs.get_next()
except errors.OutOfRangeError:
logging.warning(
'Your dataset iterator ran out of data interrupting testing. '
'Make sure that your dataset can generate at least `steps` batches '
'(in this case, %d batches).', steps)
break
if not isinstance(next_element,
(list, tuple)) or len(next_element) != 2:
raise ValueError(
'Please provide data as a list or tuple of 2 elements '
' - input and target pair. Received %s' % next_element)
x, y = next_element
# Validate and standardize data.
x, y, sample_weights = model._standardize_user_data(x, y)
# Calculate model output, loss values.
loss_outs, loss, loss_metrics = _model_loss(
model, x, y, sample_weights=sample_weights, training=False)
metrics_results = _eager_metrics_fn(model, loss_outs, y)
batch_outs = []
for _, v in zip(model.metrics_names,
[backend.mean(loss)] + loss_metrics + metrics_results):
batch_outs.append(tensor_util.constant_value(v))
# Get current step size.
if isinstance(x, list):
step_size = x[0].get_shape().as_list()[0]
else:
step_size = x.get_shape().as_list()[0]
# Accumulate results in output array.
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step_index == 0:
for _ in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
outs[i] += batch_out * step_size
# Calculate sample size.
num_samples += step_size
if verbose == 1:
progbar.update(step_index + 1)
for i in range(len(outs)):
outs[i] /= num_samples
if len(outs) == 1:
return outs[0]
return outs